Data Structures & Algorithms

Master the fundamentals of computer science with interactive problem-solving

What are Data Structures?

Data structures are specialized formats for organizing, processing, retrieving, and storing data. They provide a means to manage large amounts of data efficiently for uses such as large databases and internet indexing services.

Arrays: Contiguous memory storage for elements
Linked Lists: Dynamic data structure with nodes
Trees: Hierarchical data organization
Graphs: Network of interconnected nodes

Asymptotic Notation

Asymptotic notation is used to describe the performance or complexity of an algorithm. It helps us understand how the algorithm scales with input size.

O(1)
Constant Time - Best performance
O(log n)
Logarithmic Time - Very good
O(n)
Linear Time - Good
O(n²)
Quadratic Time - Poor
O(2ⁿ)
Exponential Time - Very poor

Time Complexity

Time complexity measures the amount of time an algorithm takes to complete as a function of the length of the input. It helps us choose the most efficient algorithm for our needs.

Searching: Binary Search (O(log n)) vs Linear Search (O(n))
Sorting: Quick Sort (O(n log n)) vs Bubble Sort (O(n²))
Access: Array access (O(1)) vs Linked List traversal (O(n))

Space Complexity

Space complexity measures the amount of memory an algorithm uses relative to the input size. It's crucial for memory-constrained environments.

In-place algorithms: Use O(1) extra space
Recursive algorithms: Use O(n) space due to call stack
Dynamic programming: Often uses O(n) or O(n²) space

All Problems

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